Integrated Spatio-Temporal Data
Mining for Network Complexity


 Tao Cheng


Senior Lecturer
Department of Civil, Environm...
Dr Tao Cheng – Background
• Data quality and uncertainty                    [1]
  of spatial objects
• Multi-scale spatio-...
Outline

 • Why integrated spatio-temporal data mining?
 • Existing ST analysis methods
   – STARIMA, ANN, SVM
 • Our appr...
Characteristics of ST Data
 • Dynamic, multi-dimensional, multi-scale
 • Spatial dependence
   “Everything is related to e...
Existing ST analysis methods
 • time series analysis + spatial correlation
 • spatial statistics + the time dimension
 • t...
Principle of ST Modelling
 Space-time data = global (deterministic) space-time trends +
                          local (s...
Model 1 - STARIMA - Spatio-Temporal Auto-
Regressive Integrated Moving Average
                 p   mk                    ...
Model 2 - ANN - Artificial Neural Networks
                (Mandic D P and Chambers JA, 2001)
SFNN – spatial interpolation...
• ANN for space-time trend analysis




                                                 n
                               ...
Model 3 – SVM - Support Vector Machines




SVC & SVR (Vapnik et al, 1996)
Our approach – Integrated modelling of ST
Model 1 – STARIMA
                  p   mk                        q    nl

    z...
Model 2 - Hybrid Modelling

                  Z i (t ) = μ i (t ) + ei (t )

                      ANN     to             ...
Model 3 - STANN




                                                      n
                Space-Time Neuron          z i...
Model 4 - STSVR


• Nonlinear Spatio-Temporal Regression by SVM



•   Develop ST kernel function
•   Overcome over-fittin...
Case Study: 194 meteorological stations
Case Study – Observations (1951 – 2002)
Nonlinear space-time trends captured by the ANN model – (a) fitted (b) predicted
Predicted (a) STARIMA model (b) Hybrid model (c) Real
STANN – Space-Time Forecasting Results




            (a) STARIMA (b) STANN (c) Real.
Residual maps for three fitted years 1970, 1980, and 1990




                       (a) STANN (b) STARIMA
(a) STANN model (b) STSVR model (c) Real.
STARMA   HYBRID   STANN   STSVR

Model-driven       √
Data-driven                          √       √
Hybrid               ...
Spatio‐Temporal Analysis of Network Data 
and Road Developments

Dr Tao Cheng 
CEGE  UCL  
Team (April 2009 – March 2012)
 • UCL
    – Dr Tao Cheng (PI), Senior Lecturer in GIS
    – Prof. Benjamin Heydecker (Co-I...
Aim
• To quantitatively measure road network
  performance
• To understand causes of traffic congestion
  – association be...
What’s new?
 • data-driven, mining
 • integrated space and time
   – ST associations
 • combine regression analysis with m...
ISTDM for Network Complexity
 1)   Dynamics
 2)   Spatial dependence
 3)   Spatio-temporal interactions
 4)   Heterogeneit...
London Road Networks
                       Cordons
                       Central, Inner, Outer
                       Sc...
Challenge (2) - Data issues
•   massive – 20GB monthly
•   multi-sourced related to 5 different networks
•   different sca...
Methodology: some preliminary thoughts
 • accommodate network structure (topology &
   geometry)
 • model spatio-temporal ...
Acknowledgements




                   National High-tech R&D Program
                            (863 Program)
Website



• www.cege.ucl.ac.uk
• http://standard.cege.ucl.ac.uk/
Some Developments in Space-Time Modelling with GIS Tao Cheng – University College London (U.K)
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Some Developments in Space-Time Modelling with GIS Tao Cheng – University College London (U.K)

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Some Developments in Space-Time Modelling with GIS
Tao Cheng – University College London (U.K)
Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)

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Some Developments in Space-Time Modelling with GIS Tao Cheng – University College London (U.K)

  1. 1. Integrated Spatio-Temporal Data Mining for Network Complexity Tao Cheng Senior Lecturer Department of Civil, Environmental & Geomatic Engineering University College London Email: tao.cheng@ucl.ac.uk
  2. 2. Dr Tao Cheng – Background • Data quality and uncertainty [1] of spatial objects • Multi-scale spatio-temporal data modelling and analysis • Intelligent spatio-temporal data mining [3] • Some relevant projects: – 4D GIS for decision support system [1] – Managing uncertainty and temporal updating (EU) – Experimental modelling of changing activity patterns using GIS (HK) [2] – Location-Based Services and the Beijing Olympics (HK) – Spatio-temporal data mining (PRC) [3] [2] 2
  3. 3. Outline • Why integrated spatio-temporal data mining? • Existing ST analysis methods – STARIMA, ANN, SVM • Our approach – A hybrid model – ANN + STARIMA – Space-Time Neural Networks – STANN – Space-Time Support Vector Machines – STSVM • ISTDM for network complexity?
  4. 4. Characteristics of ST Data • Dynamic, multi-dimensional, multi-scale • Spatial dependence “Everything is related to everything else, but near things are more related than distant things” — Tobler, First Law of Geography “If the presence of some quantity in a county (sampling unit) makes its presence in neighbouring counties (sampling units) more or less likely, we say that the phenomenon exhibits spatial autocorrelation” — Cliff and Ord • Temporal dependence • Heterogeneity & nonlinearity
  5. 5. Existing ST analysis methods • time series analysis + spatial correlation • spatial statistics + the time dimension • time series analysis + artificial neural networks ST dependence ≠ space + time Integrated modelling of ST is needed – • seamless & simultaneous • ST-association/autocorrelation
  6. 6. Principle of ST Modelling Space-time data = global (deterministic) space-time trends + local (stochastic) space-time variations Z i (t ) = μi (t ) + ei (t ) Z(t)=u(t)+e(t) Zi=ui+ei • zi (t ) - the observation of the data series at spatial location i and at time t; • μi (t ) - space-time patterns that explain large-scale deterministic space-time trends and can be expressed as a nonlinear function in space and time. • ei (t ) - the residual term, a zero mean space-time correlated error that explains small-scale stochastic space-time variations.
  7. 7. Model 1 - STARIMA - Spatio-Temporal Auto- Regressive Integrated Moving Average p mk q nl zi ( t ) = ∑∑ φ khW ( h ) z ( t − k ) − ∑ ∑ θ lhW ( h )ε ( t − l ) + ε ( t ) k =1 h = 0 l =1 h = 0 (Pfeifer P E and Deutsch S J, 1980)
  8. 8. Model 2 - ANN - Artificial Neural Networks (Mandic D P and Chambers JA, 2001) SFNN – spatial interpolation DRNN – time series analysis ( a ) static neuron ( b ) dynamic neuron n z i = ∑ iw ij ⋅ z j + b ˆ z( t ) = iw ⋅ z(t) + lw⋅ z(t − 1) + b ˆ ˆ j=1
  9. 9. • ANN for space-time trend analysis n μ i (t ) = f (∑ β k f (i, t ) + β 0 ) ˆ k =1 Tao Cheng, Jiaqiu Wang, Xia Li, Accommodating Spatial Associations in DRNN for Space-Time Analysis, Computers, Environment and Urban System, under review
  10. 10. Model 3 – SVM - Support Vector Machines SVC & SVR (Vapnik et al, 1996)
  11. 11. Our approach – Integrated modelling of ST Model 1 – STARIMA p mk q nl zi ( t ) = ∑∑ φ khW ( h ) z ( t − k ) − ∑ ∑ θ lhW ( h )ε ( t − l ) + ε ( t ) k =1 h = 0 l =1 h = 0 • define weights based upon spatial distance and spatial adjacency • consider anisotropy • able to model spatially continued phenomena
  12. 12. Model 2 - Hybrid Modelling Z i (t ) = μ i (t ) + ei (t ) ANN to STARIMA to model model nonlinear stochastic space-time space-time trends variations • overcome the limits of STARIMA • Stationarity • Linearility Tao Cheng, Jiaqiu Wang, Xia Li, A Hybrid Framework for Space-Time Modeling of Environmental Data, Geographical Analysis, under review
  13. 13. Model 3 - STANN n Space-Time Neuron z i (t) = ∑ iw (ji ) ⋅ z j (t − 1) + lw ( 0 ) ⋅ z i (t − 1) + b ˆ 1 ˆ j=1 • One step implementation of ANN+ STARIMA • Accommodate ST associations in ANN • Deal with nonlinearity & heterogeneity in BP learning Jiaqiu Wang, Tao Cheng, STANN – Modeling Space-Time Series by Artificial Neural Networks, International Journal of Geographical Information Science, under review
  14. 14. Model 4 - STSVR • Nonlinear Spatio-Temporal Regression by SVM • Develop ST kernel function • Overcome over-fitting in STANN • Deal with errors • Model nonlinearity & heterogeneity
  15. 15. Case Study: 194 meteorological stations
  16. 16. Case Study – Observations (1951 – 2002)
  17. 17. Nonlinear space-time trends captured by the ANN model – (a) fitted (b) predicted
  18. 18. Predicted (a) STARIMA model (b) Hybrid model (c) Real
  19. 19. STANN – Space-Time Forecasting Results (a) STARIMA (b) STANN (c) Real.
  20. 20. Residual maps for three fitted years 1970, 1980, and 1990 (a) STANN (b) STARIMA
  21. 21. (a) STANN model (b) STSVR model (c) Real.
  22. 22. STARMA HYBRID STANN STSVR Model-driven √ Data-driven √ √ Hybrid √ Linear √ √ Nonlinear √ √ Stationary √ √ √ √ Nonstationary √ √ √ Space/Time √ √ √ √ Discrete Space Continuous Tim Discrete √ √ √
  23. 23. Spatio‐Temporal Analysis of Network Data  and Road Developments Dr Tao Cheng  CEGE  UCL  
  24. 24. Team (April 2009 – March 2012) • UCL – Dr Tao Cheng (PI), Senior Lecturer in GIS – Prof. Benjamin Heydecker (Co-I), Professor of Transport Studies – Dr Jingxin Dong, Transport Modelling (F1) – Dr Jiaqiu Wang, GIS (F2) – RS, MSc in GIS – SVM/GWR – EngD, MSc in Transport – Simulation – 3 visiting scholars, each 2 months Other PhDs – Mr Berk Anbaroglu (RS), BSc in Computer Science – outlier detection – Ms Garavig Tanaksaranond (RS), MSc in GIS – dynamic visualization • TfL RNP&R – Mr Andy Emmonds, Principal Transport Analyst – Mr Mike Tarrier, Head of RNP&R – Mr Jonathan Turner, Performance Analyst
  25. 25. Aim • To quantitatively measure road network performance • To understand causes of traffic congestion – association between traffic and interventions • traffic flow, speed/journey time • incidents, road works, signal changes and bus lane changes • Case study – London
  26. 26. What’s new? • data-driven, mining • integrated space and time – ST associations • combine regression analysis with machine learning – improve the sensitivity and explanatory power • study the heterogeneity and scale of road performance – optimal scale for monitoring
  27. 27. ISTDM for Network Complexity 1) Dynamics 2) Spatial dependence 3) Spatio-temporal interactions 4) Heterogeneity Modelling spatiality and spatio- temporal dependence (autocorrelation) of networks is the bottleneck.
  28. 28. London Road Networks Cordons Central, Inner, Outer Screenlines Thames, Northern, five radials four peripherals
  29. 29. Challenge (2) - Data issues • massive – 20GB monthly • multi-sourced related to 5 different networks • different scales (density & frequency) • variable data quality • contain conflicts, errors, mistakes and gaps
  30. 30. Methodology: some preliminary thoughts • accommodate network structure (topology & geometry) • model spatio-temporal correlation • investigate network heterogeneity – STGWR • model impacts of interventions – STARIMA & DRNN; hybrid; STANN • Traffic pattern clustering and long-term prediction – STANN; STSVM • sensitivity analysis and accuracy assessment • simulate congestion in the short term
  31. 31. Acknowledgements National High-tech R&D Program (863 Program)
  32. 32. Website • www.cege.ucl.ac.uk • http://standard.cege.ucl.ac.uk/

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